Identifying the best performers from the 200 wheat lines based on heat tolerance of leaf carbon exchange

GRDC Heat Tolerance Project

BDSI Technical Report Series

Report ID: 2024-01-01

Report prepared by

Emi Tanaka

Biological Data Science Institute

Australian National University

Date

15th January 2024 (Last modified: 15th January 2024)

Executive Summary

The performances of the wheat lines are presented based on the single-trial, single-trait analysis of the 2023 screening data of photosynthesis, respiration and T_crit.

Note

This HTML report is best viewed from Google Chrome web browser. If you would like to save this report locally, press CTRL + S (for PC) or CMD + S for Mac.

Abbreviation	Description
°C	Degree Celsius
AAGI	Analytics for the Australian Grains Industry
GEBV	Genomic Breeding Values
GRDC	Grains Research and Development Corporation
T_crit	Critical temperature of Photosystem II disruption
TOS	Time of sowing

1 Overview

GRDC Heat Tolerance project aims to identify wheat lines that have higher heat tolerance of leaf carbon exchange. An experiment was conducted with approximately 200 wheat lines screened for multiple traits (of which photosynthesis, respiration and T_crit were of interest) at multiple locations with up to two TOS. The aim of this report is to provide the GEBV per trial as well as overall GEBV for each trait to advise the University of Sydney and Intergrain for their field trials in 2024.

2 Data description

The data used in this report originates from the 2023 field trials conducted in four sites (Griffith, Merredin, Narrabri and Parkes) with one or two sowing times (referred to as TOS1 and TOS2 hereafter) in Australia. In this report, the combination of site location and TOS is referred to as a trial. In total, there were six trials as listed in Table 1.

Table 1: The list of trials (uniquely identified by site location and time of sowing) and the physiological traits measured for the corresponding trial.

Trial	Photosynthesis	Respiration	T_crit
Griffiths TOS2
Merredin TOS1			X
Merredin TOS2			X
Narrabri TOS1
Narrabri TOS2
Parkes TOS1

2.1 Genotype

In this report, the term genotype is used synonymously as line. The experiment involved a total of 203 wheat genotypes with every trial sowing approximately 200 genotypes. Almost all genotypes were sowed at each trial (see Table 2, Table 3, and Table 4 for the genotype concurrence across trials for photosynthesis, respiration and T_crit, respectively). Note that the T_crit was not screened at Merredin.

Table 2: The concurrence of genotypes across trials for photosynthesis after renaming some genotypes (see Section 7.2) and removing certain data points (see Section 7.3). Note that the original design had exactly 200 lines per trial (see Section 7.1)

	Griffith TOS2 23	Merredin TOS1 23	Merredin TOS2 23	Narrabri TOS1 23	Narrabri TOS2 23	Parkes TOS1 23
Griffith TOS2 23	199	199	199	194	193	186
Merredin TOS1 23	199	199	199	194	193	186
Merredin TOS2 23	199	199	199	194	193	186
Narrabri TOS1 23	194	194	194	198	197	182
Narrabri TOS2 23	193	193	193	197	197	181
Parkes TOS1 23	186	186	186	182	181	186

Table 3: Same as Table 2 except for respiration.

	Griffith TOS2 23	Merredin TOS1 23	Merredin TOS2 23	Narrabri TOS1 23	Narrabri TOS2 23	Parkes TOS1 23
Griffith TOS2 23	199	199	199	194	194	199
Merredin TOS1 23	199	199	199	194	194	199
Merredin TOS2 23	199	199	199	194	194	199
Narrabri TOS1 23	194	194	194	198	198	194
Narrabri TOS2 23	194	194	194	198	198	194
Parkes TOS1 23	199	199	199	194	194	199

Table 4: Same as Table 2 except for T_crit.

	Griffith TOS2 23	Narrabri TOS1 23	Narrabri TOS2 23	Parkes TOS1 23
Griffith TOS2 23	199	194	194	198
Narrabri TOS1 23	194	198	198	193
Narrabri TOS2 23	194	198	198	193
Parkes TOS1 23	198	193	193	198

2.2 Traits

There were many traits recorded, however, the focus of this report is the three physiological traits of interest: photosynthesis, respiration and T_crit. None of these traits are directly observed; the traits of interest are all estimated from the raw data measured by the instruments in the second phase of the experiment. It should be noted that there are different ways of estimating and calibrating these traits, thus multiple measurements are recorded for each trait. The measurement used in the data sheet is given in Table 5.

Table 5: The traits of interest, the code in the data sheet, and what value is desired for heat tolerance.

Trait	Code	Desired
Photosynthesis	`AMaxAreaRaw`	High
Respiration	`RespirationAreaAdjSloRan`	Low
T_crit	`Tcrit`	High

A sample reading from the instruments are shown in Figure 1. For photosynthesis, the values are read at the start and end times with temperature kept at 35°C and the slope from the connection of the two points is used to estimate the photosynthesis. For respiration, the slope of the linear model fitted to the reading of the instrument over time since 1 hour of the instrument is in operation. This slope is converted to a respiration area. For T_crit, the fluorescence value is read across varying temperature and a break point analysis is used to estimate the temperature at which the fluorescence value changes suddenly. All the processing of the raw instrument data to phenotype data was conducted by Dr. Andrew Scafaro.

Figure 1: An example of the raw data measured by the instruments for each of the traits of interest. The data shown is from Griffith TOS2, Plate 1, Day 1 for (A) photosynthesis (at 35°C), (B) respiration (at 35°C) and (C) T_crit. The blue line in (A) connects the two points. The blue line in (B) is the fitted linear model. The estimated T_crit is shown by the red vertical line in (C).

2.3 Multi-phase experimental structure

There were two phases in the experiment: the field phase followed by screening phase. Both phases followed the experimental design provided by AAGI with appropriate randomisation and replication. The field phase involved sowing of wheat genotypes with one or two replicates (inadvertently, there were some that had three or four replicates due to renaming of genotypes as discussed in Section 7.2). Table 6, Table 7, and Table 8 show the genotype replications in the field phase for photosynthesis, respiration and T_crit, respectively.

Table 6: The table below shows the number of genotypes for each number of replication by trial for photosynthesis after removal or fixing of the data. The last columns shows the total number of field plots. There are very low counts for three and four replicates as there were some genotypes that were renamed (see Table 20).

	Number of genotypes
Trial	One replicate	Two replicates	Three replicates	Four replicates	Total number of plots
Griffith TOS2 23	100	98	0	1	300
Merredin TOS1 23	100	98	0	1	300
Merredin TOS2 23	99	99	1	0	300
Narrabri TOS1 23	0	196	0	2	400
Narrabri TOS2 23	10	185	0	2	388
Parkes TOS1 23	105	80	0	1	269

Table 7: Same as Table 6 but for respiration.

	Number of genotypes
Trial	One replicate	Two replicates	Three replicates	Four replicates	Total number of plots
Griffith TOS2 23	100	98	0	1	300
Merredin TOS1 23	100	98	0	1	300
Merredin TOS2 23	99	99	1	0	300
Narrabri TOS1 23	0	196	0	2	400
Narrabri TOS2 23	0	196	0	2	400
Parkes TOS1 23	100	98	0	1	300

Table 8: Same as Table 6 but for T_crit.

	Number of genotypes
Trial	One replicate	Two replicates	Four replicates	Total number of plots
Griffith TOS2 23	100	98	1	300
Narrabri TOS1 23	0	196	2	400
Narrabri TOS2 23	0	196	2	400
Parkes TOS1 23	99	98	1	299

The screening phase involved taking the leaves from the field plot and laying it out onto the plate to measure with instruments. The total number of days, the number of plates and the dimension of the plate along with the information of replication (either at field or screening phase) by trial is shown in Table 9, Table 10, and Table 11 for photosynthesis, respiration and T_crit, respectively.

Table 9: The table below shows the structure of the screening phase for photosynthesis. For each trial, the total number of days, the number of plates per day, and the dimension of the plates are shown. Furthermore, the number of plots for the corresponding number of genotype replications in the field and plot replications in the screening phase by trial after renaming some genotypes (see Table 20) and removing data points unused in the analysis (see Section 7.3). E.g. the row with a field replication of 2 and screening replication of 2 corresponds to the number of plots that has two genotype replicates in the field and two well replicates.

			Plate dimension		Replications		Number of
Trial	Days	Plates	Rows	Columns	Field	Screening	Plots	Observations
Griffith TOS2 23	3	6	6	4	1	1	55	396
						2	45
					2	1	146
						2	50
					4	1	3
						2	1
Merredin TOS1 23	3	6	6	4	1	1	55	396
						2	45
					2	1	146
						2	50
					4	1	3
						2	1
Merredin TOS2 23	3	6	6	4	1	1	53	396
						2	46
					2	1	149
						2	49
					3	1	2
						2	1
Narrabri TOS1 23	5	6	6	4	2	1	138	660
						2	254
					4	1	2
						2	6
Narrabri TOS2 23	5	6	6	4	1	1	2	616
						2	8
					2	1	154
						2	216
					4	1	4
						2	4
Parkes TOS1 23	3	6	6	4	1	1	66	340
						2	39
					2	1	128
						2	32
					4	1	4

Table 10: Same as Table 9 but for respiration.

			Plate dimension		Replications		Number of
Trial	Days	Plates	Rows	Columns	Field	Screening	Plots	Observations
Griffith TOS2 23	3	3	6	8	1	1	55	396
						2	45
					2	1	146
						2	50
					4	1	3
						2	1
Merredin TOS1 23	3	3	6	8	1	1	56	393
						2	44
					2	1	148
						2	48
					4	1	3
						2	1
Merredin TOS2 23	3	3	6	8	1	1	53	396
						2	46
					2	1	149
						2	49
					3	1	2
						2	1
Narrabri TOS1 23	5	3	6	8	2	1	138	660
						2	254
					4	1	2
						2	6
Narrabri TOS2 23	5	3	6	8	2	1	137	660
						2	255
					4	1	3
						2	5
Parkes TOS1 23	3	3	6	8	1	1	52	396
						2	48
					2	1	148
						2	48
					4	1	4

Table 11: Same as Table 9 but for T_crit.

			Plate dimension		Replications		Number of
Trial	Days	Plates	Rows	Columns	Field	Screening	Plots	Observations
Griffith TOS2 23	3	3	6	8	1	1	55	396
						2	45
					2	1	146
						2	50
					4	1	3
						2	1
Narrabri TOS1 23	5	3	6	8	2	1	138	660
						2	254
					4	1	2
						2	6
Narrabri TOS2 23	5	3	6	8	2	1	137	660
						2	255
					4	1	3
						2	5
Parkes TOS1 23	3	3	6	8	1	1	53	391
						2	46
					2	1	150
						2	46
					4	1	4

2.4 Phenotype distribution

Figure 2 shows the phenotype distribution of photosynthesis, respiration, and T_crit as measured by coded variable in brackets by trial. The distribution of photosynthesis and respiration are skewed to the right while the distribution of T_crit is skewed to the left. The distribution of photosynthesis is similar across trials.

Figure 2: The figure above shows the phenotype distribution of photosynthesis, respiration, and T_crit as measured by coded variable in brackets by trial. For T_crit, the size is inversely proportional to its standard error (thus larger points are more reliable estimates). Dragging and selecting a region will highlight the points with the same genotype in all figures. The drop down menu on the top of this figure will show the name of the genotype that was selected. The drop down menu can be used to also select a particular set of genotypes. Double clicking should reset the selection.

Table 12: The data files used in this report. The second column shows the last modified date of the file. The rows are ordered by the modified date. This is just for quality check purposes to ensure the wrong files were not used in the analysis.

File name	Last modified
Mer_TOS1_23_Q2_Respiration.xlsx	2023-12-21 09:34:50
Mer_TOS1_23_Q2_Photosynthesis.xlsx	2023-12-21 09:36:17
Mer_TOS2_23_Q2_Respiration.xlsx	2023-12-21 09:37:15
Mer_TOS2_23_Q2_Photosynthesis.xlsx	2023-12-21 09:37:54
Nar_TOS1_23_Q2_Respiration.xlsx	2023-12-21 09:40:10
Nar_TOS1_23_Q2_Photosynthesis.xlsx	2023-12-21 09:40:51
Nar_TOS2_23_Q2_Respiration.xlsx	2023-12-21 09:41:33
Par_TOS1_23_Q2_Photosynthesis.xlsx	2023-12-22 09:39:19
NAR_TOS1_23_Tcrit.xlsx	2023-12-22 09:49:25
NAR_TOS2_23_Tcrit.xlsx	2023-12-22 09:52:45
Gri_TOS2_23_Q2_Photosynthesis.xlsx	2024-01-02 13:47:44
Nar_TOS2_23_Q2_Photosynthesis.xlsx	2024-01-03 11:11:17
GRI_TOS2_23_Tcrit.xlsx	2024-01-05 13:56:17
PAR_TOS1_23_Tcrit.xlsx	2024-01-08 16:29:43
PAR_TOS1_23_Q2_Respiration.xlsx	2024-01-10 14:18:55
Gri_TOS2_23_Q2_Respiration.xlsx	2024-01-10 15:47:08

3 Statistical methods

The trait of interest is analysed using a linear mixed model using ASReml-R version 4.2.0.267 (Butler et al. 2023).

Warning

The models fitted are equivalent to single trial baseline analysis for each trait. The genotype performance prediction does not borrow information across trials, nor is the data analysed using a multi-trait model, which can also potentially improve the prediction if the traits are correlated. The residual plots suggest that not all unexplained variation is explained in the model. This effects the calculations of the standard error in the prediction and the estimates of heritability, so some caution should be taken into account in its interpretation.

The following tabs show the fitted models for each trait and the corresponding estimated variance components in Table 13, Table 14, and Table 15. The heritability for each trait by trial is estimated as

\[\frac{\sigma^2_g}{\sigma^2_g + \sigma^2}\] where \(\sigma^2_g\) is the variance component estimate for the Genotype term and \(\sigma^2\) is the residual variance. The heritability estimates are shown in the Genotype row in the Table 13, Table 14, and Table 15. Note that there are other (and likely better) ways of estimating heritability (see Piepho and Möhring 2007).

asreml(fixed = AMaxAreaRaw ~ site, random = ~diag(site):PlotRow + 
    diag(site):PlotRange + diag(site):Day + diag(site):Plate + 
    diag(site):Day:Plate + diag(site):Day:Plate:PCol + diag(site):Day:Plate:PRow + 
    diag(site):Day:Plate + diag(site):PlotRow:PlotRange + diag(site):Genotype, 
    residual = ~dsum(~units | site), data = photosynthesisII)

Table 13: The table below shows the variance component estimates and its standard errors (written in bracket) from the fitted model for the photosynthesis by trial. The first column shows the associated terms in the model. The Genotype row also contains the estimated heritability. Where there is no entry, the estimate of the corresponding term was virtually nil.

Terms	Griffith TOS2 23	Merredin TOS1 23	Merredin TOS2 23	Narrabri TOS1 23	Narrabri TOS2 23	Parkes TOS1 23
Day		0.378 (0.775)	1.928 (2.156)	1.781 (1.606)		0.550 (0.637)
Plate		0.291 (0.711)	0.018 (0.350)			0.751 (0.585)
Day:Plate	1.508 (0.967)	1.182 (0.980)	0.676 (0.603)	2.084 (0.802)	1.803 (0.731)
PlotRange	0.027 (0.237)	0.513 (0.487)			0.500 (0.465)	0.041 (0.166)
PlotRow			0.504 (0.284)	0.231 (0.324)	0.858 (0.534)
Day:Plate:PCol	1.128 (0.740)	0.836 (0.721)	1.566 (0.500)			0.000 (0.360)
Day:Plate:PRow	1.825 (0.887)	0.374 (0.759)
Genotype	2.570 (1.361) Heritability: 21.0%	2.135 (1.172) Heritability: 18.1%	0.541 (0.430) Heritability: 5.3%	1.560 (0.722) Heritability: 13.9%	0.380 (1.014) Heritability: 3.8%
PlotRow:PlotRange	0.858 (1.929)				1.278 (1.679)
Residual	10.721 (1.774)	13.235 (1.613)	4.659 (0.540)	15.198 (1.049)	17.129 (1.564)	9.648 (0.824)

asreml(fixed = RespirationAreaAdjSloRan ~ site, random = ~diag(site):PlotRow + 
    diag(site):PlotRange + diag(site):Day + diag(site):Plate + 
    diag(site):Day:Plate + diag(site):Day:Plate:PCol + diag(site):Day:Plate:PRow + 
    diag(site):PlotRow:PlotRange + diag(site):Genotype, residual = ~dsum(~units | 
    site), data = respirationII)

Table 14: Same as Table 13 except the fitted model is for respiration.

Terms	Griffith TOS2 23	Merredin TOS1 23	Merredin TOS2 23	Narrabri TOS1 23	Narrabri TOS2 23	Parkes TOS1 23
Plate			0.823 (0.912)			0.000 (0.002)
Day	0.009 (0.010)	0.094 (0.096)		0.001 (0.008)	0.032 (0.037)	0.201 (0.203)
Day:Plate	0.000 (0.001)	0.002 (0.004)		0.015 (0.012)	0.052 (0.026)	0.001 (0.003)
PlotRange	0.000 (0.001)				0.025 (0.010)	0.001 (0.001)
PlotRow	0.000 (0.001)		0.002 (0.119)	0.005 (0.007)
Day:Plate:PRow			0.457 (0.185)			0.011 (0.003)
Day:Plate:PCol	0.000 (0.002)	0.008 (0.008)	0.924 (0.263)	0.029 (0.012)		0.002 (0.001)
Genotype	0.004 (0.003) Heritability: 19.1%	0.010 (0.012) Heritability: 34.2%	0.154 (0.418) Heritability: 89.4%	0.005 (0.016) Heritability: 20.6%	0.037 (0.015) Heritability: 67.0%	0.001 (0.003) Heritability: 5.3%
PlotRow:PlotRange			0.190 (0.631)	0.041 (0.026)	0.009 (0.020)	0.008 (0.004)
Residual	0.042 (0.004)	0.173 (0.018)	2.782 (0.504)	0.255 (0.024)	0.227 (0.019)	0.018 (0.003)

asreml(fixed = Tcrit ~ site, random = ~diag(site):PlotRow + diag(site):PlotRange + 
    diag(site):Day + diag(site):Plate + diag(site):Day:Plate + 
    diag(site):Day:Plate:PCol + diag(site):Day:Plate:PRow + diag(site):PlotRow:PlotRange + 
    diag(site):Genotype, residual = ~dsum(~units | site), data = fluorometerII)

Table 15: Same as Table 13 except the fitted model is for T_crit.

Terms	Griffith TOS2 23	Narrabri TOS1 23	Narrabri TOS2 23	Parkes TOS1 23
Plate		1.133 (1.217)	0.547 (0.673)	0.385 (0.725)
Day	0.001 (0.127)	0.587 (0.519)
Day:Plate	0.294 (0.189)	0.384 (0.209)	0.587 (0.255)	0.380 (0.566)
PlotRow	0.012 (0.024)
PlotRange	0.034 (0.029)
Day:Plate:PRow	0.060 (0.043)	0.022 (0.032)	0.060 (0.037)
Day:Plate:PCol		0.027 (0.036)		2.722 (0.826)
Genotype		0.135 (0.054) Heritability: 1.7%	0.023 (0.061) Heritability: 0.3%	2.327 (1.080) Heritability: 23.0%
PlotRow:PlotRange			0.241 (0.105)	0.116 (1.544)
Residual	0.996 (0.080)	1.085 (0.082)	0.960 (0.093)	7.810 (1.355)

The GEBV is calculated by using the prediction of genotype and trial combination for each of the trait as per Welham et al. (2004). If there were no measurable genotype variance for the trait then no GEBV is given.

The ratio of photosynthesis over respiration is predicted using the GEBV of photosynthesis and respiration. The standard error of the ratio is calculated using the delta method.

The overall GEBV for each trait is calculated by using the inverse-variance weighting (the weighted average of the GEBV across trials where weight corresponds to the inverse of the variance of the GEBV). The corresponding standard error was estimated assuming that the standard errors of the GEBV are known. This means that the standard error of the overall GEBV is likely underestimated.

4 Key results

The results are presented as either interactive plots or interactive table for GEBV by trial and for overall GEBV. The link to download the results as CSV are provided below.

4.1 GEBV by trial

Figure 3 shows visually the GEBV by trial along with the trait information. The most desired genotypes would be the points residing in the top left quadrant of most plots that is yellowish in color (of which there are not many) with a large size. There is no clear stable, stand-out performers.

Figure 3: The plot above is a scatter plot where each point is the genotype means at each trial as predicted from the fitted model presented in Section 3. The horizontal axis is the predicted value of respiration, the vertical axis is the predicted value of photosynthesis, the size corresponds to the inverse of the standard error of the ratio of photosynthesis over respiration estimated using the delta method, and the color corresponds to the predicted value of T_crit. If a particular trait had no heritability, the trait is omitted from the plot, e.g. the vertical axis has no meaning for Parkes TOS1. Hovering over this plot will display further information about the corresponding point. If you drag and select, all points with the same genotype will be highlighted in other trials. A small sized point means that the prediction is less reliable. The selected genotype names can be found on top of Figure 2. To reset the selection, double click on the plot.

The results of GEBV by trial, seen in Table 16, can be downloaded from the link below.

Download CSV for GEBV by trial

Table 16: The table below shows the predicted values, associated standard error, and ranking for each trait by trial and genotype.

4.2 Overall GEBV

Figure 4 shows the overall GEBV in a style similar to Figure 3. The overall trait GEBV against its rank is shown in Figure 5.

Figure 4: The figure above shows the overall predicted values. Drag and selecting a region will highlight genotypes that are the same in all other figures. Dragging and selecting a region will highlight genotypes that are the same in all other figures. Either see the top of Figure 2 for the names of the selected genotypes or hover over the point with the mouse.

Figure 5: The figure above shows the overall GEBV against their ranks. Dragging and selecting a region will highlight genotypes that are the same in all other figures. Either see the top of Figure 2 for the names of the selected genotypes or hover over the point with the mouse.

The results of overall GEBV, seen in Table 17, can be downloaded from the link below.

Download CSV for overall GEBV

The standard error of the overall GEBV for respiration is noticeably very high. This is possibly due to the unreliableness of this measure.

Table 17: The table below shows the overall GEBV estimated using the inverse-variance weighting of GEBV by trials, associated standard error, and ranking for each trait. If any points are highlighted red in other figures, this table will subset to only show those genotypes.

5 Conclusion

Every effort is made to make the results (including numerical results in the inline text) reproducible, but the results can change when there is an update to the data. For this reason, I recommend checking Table 12 to check the last modified date of the data files.

Due to lack of time and capacity, as mentioned in Section 3, the models fitted are equivalent to single trial baseline analysis for each trait. The genotype performance prediction does not borrow information across trials, nor is the data analysed using a multi-trait model, which can also potentially improve the prediction if the traits are correlated. The residual plots suggest that not all unexplained variation is explained in the model. This effects the calculations of the standard error in the prediction and the estimates of heritability, so some caution should be taken into account in its interpretation.

If an appropriate factor analytic mixed model for the multi-environmental trial was fitted, the method in Smith and Cullis (2018) may be a better approach to calculating the overall GEBV.

6 Acnowledgements

Many thanks to Andrew Bowerman, Andrew Scafaro and Frederike Stock for answering all my questions related to the data.

This report is written reproducibly using Quarto. For the details of the computational tools used, see Section 7.5.

7 Appendix

7.1 Original experimental design

Due to the renaming of genotypes and removal of data points, the number of genotype replications across different phases of the experiment is now different. In the original experimental design, there were exactly 200 genotypes per trial (Table 18) and exactly half of the genotype were replicated once while the other half twice in the field phase, except Narrabri where all genotypes were replicated twice (Table 19).

Table 18: The genotype concurrence across trials for the original experimental design for photosynthesis.

	Griffith TOS2 23	Merredin TOS1 23	Merredin TOS2 23	Narrabri TOS1 23	Narrabri TOS2 23	Parkes TOS1 23
Griffith TOS2 23	200	200	200	191	191	200
Merredin TOS1 23	200	200	200	191	191	200
Merredin TOS2 23	200	200	200	191	191	200
Narrabri TOS1 23	191	191	191	200	200	191
Narrabri TOS2 23	191	191	191	200	200	191
Parkes TOS1 23	200	200	200	191	191	200

Table 19: The genotype replication at field trials for the original experimental design for photosynthesis.

	Number of genotypes
Trial	One replicate	Two replicates	Total number of plots
Griffith TOS2 23	100	100	300
Merredin TOS1 23	100	100	300
Merredin TOS2 23	100	100	300
Narrabri TOS1 23	0	200	400
Narrabri TOS2 23	0	200	400
Parkes TOS1 23	100	100	300

7.2 Renaming of genotype names

Upon clarification with data custodians, there were some genotypes that should have been encoded differently. The renaming of the genotypes upon this clarification is listed in Table 20.

Table 20: The renaming of genotypes to ensure the same encoding is used consistently across trials as clarified by the data stewards. Note that this renaming means that the number of replications become different to the original intended experimental design.

Old name	New name
IG-ANU-HeatLine-028-2	IG-ANU-HeatLine-028
IG-ANU-HeatLine-028-1	IG-ANU-HeatLine-028
ZWB13-171-1	ZWB13-171
ZWB13-171-2	ZWB13-171
ZWW10-080-1	ZWW10-080
ZWW10-080-2	ZWW10-080

7.3 Removed data points

The following data points were removed due to actual or potential data quality issues. These include the following issues for photosynthesis as reported by Dr. Andrew Scafaro:

Narrabri TOS2, Day 2, Plates 1 and 2, 25°C and 35°C (lights not turned off during measurement which led to incorrect fluorophore reading).
Parkes TOS1, Day 1, Plates 1 and 2, 35°C, Plate Column 4 only (instrument malfunctioned and did not read the last column of the 35 bay for this run).
Parkes TOS1, Day 3, Plates 3 and 4, 25°C and 35°C (data was lost).

In addition to above, I eye-balled the visualisation of all the raw data and deemed some to be unreliable. The full list of remove data points are listed in Table 21.

Table 21: The list of data points that were removed from analysis.

7.4 Genotypes not present in all trials

Table 22 lists the genotypes that are absent in a subset of trials.

Table 22: The genotypes that are not present in all trials. The first column list the name of the genotype, the tick shows where the genotype was present and cross where it was absent.

Genotype	Griffith TOS2 23	Merredin TOS1 23	Merredin TOS2 23	Narrabri TOS1 23	Narrabri TOS2 23	Parkes TOS1 23
Jillaroo				X	X
IG-ANU-HeatLine-115				X	X
IG-ANU-HeatLine-114				X	X
Brumby				X	X
IG-ANU-HeatLine-113				X	X
Alatheer-4	X	X	X			X
Shamiekh-3	X	X	X			X
ISR812.8/CARINYA	X	X	X			X
PBI16C001-0C-0N-0N-010N-12N	X	X	X			X

7.5 Computational tools

This report was written using Quarto version 1.4.502. The analysis was conducted using R and numerous R packages. The versions of the tools used are shown below.

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.1 (2023-06-16)
 os       macOS Sonoma 14.2.1
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Australia/Sydney
 date     2024-01-15
 pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package      * version   date (UTC) lib source
 archive        1.1.7     2023-12-11 [1] CRAN (R 4.3.1)
 askpass        1.2.0     2023-09-03 [1] CRAN (R 4.3.0)
 asreml       * 4.2.0.267 2023-07-05 [1] local
 beeswarm     * 0.4.0     2021-06-01 [1] CRAN (R 4.3.0)
 bit            4.0.5     2022-11-15 [1] CRAN (R 4.3.0)
 bit64          4.0.5     2020-08-30 [1] CRAN (R 4.3.0)
 bslib          0.6.1     2023-11-28 [1] CRAN (R 4.3.1)
 cachem         1.0.8     2023-05-01 [1] CRAN (R 4.3.0)
 cellranger     1.1.0     2016-07-27 [1] CRAN (R 4.3.0)
 cli            3.6.2     2023-12-11 [1] CRAN (R 4.3.1)
 codetools      0.2-19    2023-02-01 [1] CRAN (R 4.3.1)
 colorspace     2.1-0     2023-01-23 [1] CRAN (R 4.3.0)
 commonmark     1.9.0     2023-03-17 [1] CRAN (R 4.3.0)
 crayon         1.5.2     2022-09-29 [1] CRAN (R 4.3.0)
 crosstalk      1.2.1     2023-11-23 [1] CRAN (R 4.3.1)
 data.table     1.14.10   2023-12-08 [1] CRAN (R 4.3.1)
 digest         0.6.33    2023-07-07 [1] CRAN (R 4.3.0)
 dplyr        * 1.1.4     2023-11-17 [1] CRAN (R 4.3.1)
 DT           * 0.31      2023-12-09 [1] CRAN (R 4.3.1)
 ellipsis       0.3.2     2021-04-29 [1] CRAN (R 4.3.0)
 evaluate       0.23      2023-11-01 [1] CRAN (R 4.3.1)
 expm           0.999-9   2024-01-11 [1] CRAN (R 4.3.1)
 fansi          1.0.6     2023-12-08 [1] CRAN (R 4.3.1)
 farver         2.1.1     2022-07-06 [1] CRAN (R 4.3.0)
 fastmap        1.1.1     2023-02-24 [1] CRAN (R 4.3.0)
 forcats      * 1.0.0     2023-01-29 [1] CRAN (R 4.3.0)
 generics       0.1.3     2022-07-05 [1] CRAN (R 4.3.0)
 ggplot2      * 3.4.4     2023-10-12 [1] CRAN (R 4.3.1)
 ggtext       * 0.1.2     2023-11-15 [1] Github (wilkelab/ggtext@c519337)
 glue           1.6.2     2022-02-24 [1] CRAN (R 4.3.0)
 gridtext       0.1.5     2022-09-16 [1] CRAN (R 4.3.0)
 gtable         0.3.4     2023-08-21 [1] CRAN (R 4.3.0)
 highr          0.10      2022-12-22 [1] CRAN (R 4.3.0)
 hms            1.1.3     2023-03-21 [1] CRAN (R 4.3.0)
 htmltools      0.5.7     2023-11-03 [1] CRAN (R 4.3.1)
 htmlwidgets    1.6.4     2023-12-06 [1] CRAN (R 4.3.1)
 httpuv         1.6.13    2023-12-06 [1] CRAN (R 4.3.1)
 httr           1.4.7     2023-08-15 [1] CRAN (R 4.3.0)
 jquerylib      0.1.4     2021-04-26 [1] CRAN (R 4.3.0)
 jsonlite       1.8.8     2023-12-04 [1] CRAN (R 4.3.1)
 kableExtra   * 1.3.4     2021-02-20 [1] CRAN (R 4.3.0)
 knitr          1.45      2023-10-30 [1] CRAN (R 4.3.1)
 labeling       0.4.3     2023-08-29 [1] CRAN (R 4.3.0)
 later          1.3.2     2023-12-06 [1] CRAN (R 4.3.1)
 lattice        0.22-5    2023-10-24 [1] CRAN (R 4.3.1)
 lazyeval       0.2.2     2019-03-15 [1] CRAN (R 4.3.0)
 lifecycle      1.0.4     2023-11-07 [1] CRAN (R 4.3.1)
 lubridate    * 1.9.3     2023-09-27 [1] CRAN (R 4.3.1)
 magrittr       2.0.3     2022-03-30 [1] CRAN (R 4.3.0)
 markdown       1.12      2023-12-06 [1] CRAN (R 4.3.1)
 Matrix       * 1.6-4     2023-11-30 [1] CRAN (R 4.3.1)
 mgcv           1.9-1     2023-12-21 [1] CRAN (R 4.3.1)
 mime           0.12      2021-09-28 [1] CRAN (R 4.3.0)
 msm          * 1.7.1     2023-11-23 [1] CRAN (R 4.3.1)
 munsell        0.5.0     2018-06-12 [1] CRAN (R 4.3.0)
 mvtnorm        1.2-4     2023-11-27 [1] CRAN (R 4.3.1)
 nlme           3.1-164   2023-11-27 [1] CRAN (R 4.3.1)
 openssl        2.1.1     2023-09-25 [1] CRAN (R 4.3.1)
 patchwork    * 1.1.3     2023-08-14 [1] CRAN (R 4.3.0)
 pillar         1.9.0     2023-03-22 [1] CRAN (R 4.3.0)
 pkgconfig      2.0.3     2019-09-22 [1] CRAN (R 4.3.0)
 plotly       * 4.10.3    2023-10-21 [1] CRAN (R 4.3.1)
 processx       3.8.3     2023-12-10 [1] CRAN (R 4.3.1)
 promises       1.2.1     2023-08-10 [1] CRAN (R 4.3.0)
 ps             1.7.5     2023-04-18 [1] CRAN (R 4.3.0)
 purrr        * 1.0.2     2023-08-10 [1] CRAN (R 4.3.0)
 quarto         1.3       2023-09-19 [1] CRAN (R 4.3.1)
 R6             2.5.1     2021-08-19 [1] CRAN (R 4.3.0)
 RColorBrewer   1.1-3     2022-04-03 [1] CRAN (R 4.3.0)
 Rcpp           1.0.11    2023-07-06 [1] CRAN (R 4.3.0)
 readr        * 2.1.4     2023-02-10 [1] CRAN (R 4.3.0)
 readxl       * 1.4.3     2023-07-06 [1] CRAN (R 4.3.0)
 rlang        * 1.1.2     2023-11-04 [1] CRAN (R 4.3.1)
 rmarkdown      2.25      2023-09-18 [1] CRAN (R 4.3.1)
 rstudioapi     0.15.0    2023-07-07 [1] CRAN (R 4.3.0)
 rvest          1.0.3     2022-08-19 [1] CRAN (R 4.3.0)
 sass           0.4.8     2023-12-06 [1] CRAN (R 4.3.1)
 scales         1.3.0     2023-11-28 [1] CRAN (R 4.3.1)
 sessioninfo    1.2.2     2021-12-06 [1] CRAN (R 4.3.0)
 shiny          1.8.0     2023-11-17 [1] CRAN (R 4.3.1)
 stringi        1.8.3     2023-12-11 [1] CRAN (R 4.3.1)
 stringr      * 1.5.1     2023-11-14 [1] CRAN (R 4.3.1)
 survival       3.5-7     2023-08-14 [1] CRAN (R 4.3.0)
 svglite        2.1.3     2023-12-08 [1] CRAN (R 4.3.1)
 systemfonts    1.0.5     2023-10-09 [1] CRAN (R 4.3.1)
 tibble       * 3.2.1     2023-03-20 [1] CRAN (R 4.3.0)
 tidyr        * 1.3.0     2023-01-24 [1] CRAN (R 4.3.0)
 tidyselect     1.2.0     2022-10-10 [1] CRAN (R 4.3.0)
 tidyverse    * 2.0.0     2023-02-22 [1] CRAN (R 4.3.0)
 timechange     0.2.0     2023-01-11 [1] CRAN (R 4.3.0)
 tzdb           0.4.0     2023-05-12 [1] CRAN (R 4.3.0)
 utf8           1.2.4     2023-10-22 [1] CRAN (R 4.3.1)
 vctrs          0.6.5     2023-12-01 [1] CRAN (R 4.3.1)
 vipor          0.4.7     2023-12-18 [1] CRAN (R 4.3.1)
 viridisLite    0.4.2     2023-05-02 [1] CRAN (R 4.3.0)
 vroom          1.6.5     2023-12-05 [1] CRAN (R 4.3.1)
 webshot        0.5.5     2023-06-26 [1] CRAN (R 4.3.0)
 withr          2.5.2     2023-10-30 [1] CRAN (R 4.3.1)
 xfun           0.41      2023-11-01 [1] CRAN (R 4.3.1)
 xml2           1.3.6     2023-12-04 [1] CRAN (R 4.3.1)
 xtable         1.8-4     2019-04-21 [1] CRAN (R 4.3.0)
 yaml           2.3.8     2023-12-11 [1] CRAN (R 4.3.1)

 [1] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library

──────────────────────────────────────────────────────────────────────────────

References

Butler, D G, B R Cullis, A R Gilmour, B J Gogel, and R Thompson. 2023. “ASReml-R Reference Manual Version 4.2.”

Piepho, Hans Peter, and Jens Möhring. 2007. “Computing Heritability and Selection Response from Unbalanced Plant Breeding Trials.” Genetics 177 (3): 1881–88. https://doi.org/10.1534/genetics.107.074229.

Smith, Alison, and Brian R Cullis. 2018. “Plant Breeding Selection Tools Built on Factor Analytic Mixed Models for Multi-Environment Trial Data.” Euphytica 214 (8): 143. https://doi.org/10.1007/s10681-018-2220-5.

Welham, Sue J, Brian R Cullis, Beverley J Gogel, Arthur R Gilmour, and Robin Thompson. 2004. “Prediction in Linear Mixed Models.” Australian & New Zealand Journal of Statistics 46 (3): 325–47. https://doi.org/10.1111/j.1467-842X.2004.00334.x.